This package aims at complementing the party
and partykit
packages with parallelization and interpretation tools.
It provides functions for :
It also provides a module and a shiny app for conditional inference trees.
Execute the following code within R
:
if (!require(devtools)){
install.packages('devtools')
library(devtools)
}
install_github("nicolas-robette/moreparty")
Altmann A., Toloşi L., Sander O., and Lengauer T. “Permutation importance: a corrected feature importance measure”. Bioinformatics, 26(10):1340-1347, 2010.
Apley, D. W., Zhu J. “Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models”. arXiv:1612.08468v2, 2019.
Gregorutti B., Michel B., and Saint Pierre P. “Correlation and variable importance in random forests”. arXiv:1310.5726, 2017.
Hapfelmeier A. and Ulm K. “A new variable selection approach using random forests”. Computational Statistics and Data Analysis, 60:50–69, 2013.
Hothorn T., Hornik K., Van De Wiel M.A., Zeileis A. “A lego system for conditional inference”. The American Statistician. 60:257–263, 2006.
Hothorn T., Hornik K., Zeileis A. “Unbiased Recursive Partitioning: A Conditional Inference Framework”. Journal of Computational and Graphical Statistics, 15(3):651-674, 2006.
Molnar, C. Interpretable machine learning. A Guide for Making Black Box Models Explainable, 2019. (https://christophm.github.io/interpretable-ml-book/)
Strobl, C., Malley, J., and Tutz, G. “An Introduction to Recursive Partitioning: Rationale, Application, and Characteristics of Classification and Regression Trees, Bagging, and Random Forests”. Psychological methods, 14(4):323-348, 2009.